Dynamic Adaptive Network-Based Fuzzy Inference System (D-ANFIS) for the Imputation of Missing Data for Internet of Medical Things Applications

Abstract

Data delivery and acquisition are the main factors needed for the success of any proposed Internet of Medical Things (IoMT) systems. To achieve good performance and high quality of services in IoMT systems, data acquisition, and delivery should be performed accurately. In general, IoMT systems are usually vulnerable to the collected data with missing value(s) since missing data is the main problem that affects the overall performance of any system. This leads to a reduction in the satisfaction level of end users. Missing data for IoMT systems originates from a number of sources, including bad connections, outside attack, or sensing errors. To obtain a high performance in such systems, missing data should be imputed once occurred. In this paper, a dynamic adaptive network-based fuzzy inference system (D-ANFIS) approach is proposed to impute the missing values in a simple yet accurate manner. The major contribution is to impute the missing value(s) once received by dividing the collected data into two groups: 1) complete dataset (without missing data) and 2) incomplete dataset (with missing data). A holdout method is used to train the D-ANFIS using complete data, while the incomplete dataset is used to impute the missing value(s). Two methods are used to evaluate the final performance of IoMT application: 1) adaptive network-based fuzzy inference system (ANFIS) with genetic algorithm (ANFIS-GA) and 2) ANFIS with particle swarm optimization (ANFIS-PSO). The results show that the performance of IoMT is enhanced 5% using ANFIS-GA and 3% using ANFIS-PSO.